Rapid urbanization and continuous loss of rural labor force has resulted in abandonment of large areas of farmland in some regions of China. Remote sensing technology can indirectly help detect abandoned farmland size and quantity, which is of great significance for farmland protection and food security. This study took Qingyun and Wudi counties in Shandong Province as a study area and used CART decision tree classification to compile land use maps of 1990-2017 based on Landsat and HJ-1A data. We developed rules to identify abandoned farmland, and explored its spatial distribution, duration, and reclamation. CART accuracy exceeded 85% from 1990-2017. The maximum abandoned farmland area was 5503.86 ha during 1992-2017, with the maximum rate being 5.37%. Farmland abandonment rate was the highest during 1996-1998, and abandonment trend decreased year by year after 2006. Maximum abandonment duration was 15 years (1992-2017), mostly within 4 years and only a few exceeded 10 years. From 1993-2017, the maximum reclaimed abandoned farmland was 2022.3 ha, and the minimum ~20 ha. The maximum reclamation rate was 67.44%m, with annual average rate being 31.83%. This study will help analyze farmland abandonment driving forces in the study area and also provide references to identify abandoned farmland in other areas.

Core Ideas Dividing the study area into subregions and classifying them improved winter wheat classification accuracy.Single‐phase Normalized Difference Vegetation Indices acquired on 6 March, 23 April, 25 May, and 29 June were the best variables for building the wheat yield spatialization model.The extraction accuracy of winter wheat area greatly impacted the spatialization of yield.The proposed model can provide a technical reference for producing high‐resolution crop yield distribution maps. Grain yield data based on administrative divisions (counties, cities, etc.) for statistics lack spatial information, which can be effectively solved by grain yield spatialization. This paper proposes a spatialization method for grain yield based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series data. The method was tested by taking winter wheat (Triticum aestivum L.) in Shandong Province in China as an example. First, the classification and regression tree (CART) algorithm was trained to extract the winter wheat planting pixels in 2016. The average NDVIs of the different growing stages (returning green, jointing, heading, and milk ripening) were calculated from the MODIS NDVI time series data. The relationship between winter wheat yield and NDVI variables (including single‐phase NDVI and the average NDVI of different growing stages) was analyzed by univariate and multiple linear regressions. The NDVI variable with the highest correlation to winter wheat yield and the minimum root mean square error of the fitting equation were chosen as input to build the spatialization model. The results show that the classification accuracy of winter wheat estimated with the confusion matrix was 82.51% and that the average precision of planting acreage compared with county‐level statistical data was 87.64%. The average relative error of yield spatialization at the county level was 22.71%. The method developed in this paper is easy to operate and popularize, and it can provide a technical reference for producing high‐resolution crop yield distribution maps of long time series through spatialization.

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Tobacco planting information is an important part of tobacco production management. Unmanned aerial vehicle (UAV) remote sensing systems have become a popular topic worldwide because they are mobile, rapid and economic. In this paper, an automatic identification method for tobacco fields based on UAV images is developed by combining supervised classifications with image morphological operations, and this method was used in the Yunnan Province, which is the top province for tobacco planting in China. The results show that the produce accuracy, user accuracy, and overall accuracy of tobacco field identification using the method proposed in this paper are 92.59%, 96.61% and 95.93%, respectively. The method proposed in this paper has the advantages of automation, flow process, high accuracy and easy operation, but the ground sampling distance (GSD) of the UAV image has an effect on the accuracy of the proposed method. When the image GSD was reduced to 1 m, the overall accuracy decreased by approximately 10%. To solve this problem, we further introduced the convolution method into the proposed method, which can ensure the recognition accuracy of tobacco field is above 90% when GSD is less than or equal to 1 m. Some other potential improvements of methods for mapping tobacco fields were also discussed in this paper.